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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: openchat/openchat_3.5
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+ datasets:
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+ - HuggingFaceH4/no_robots
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+ language:
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+ - en
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+ widget:
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+ - text: |
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+ <|system|>
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+ You are a friendly chatbot who always responds in the style of a pirate</s>
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+ <|user|>
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+ How many helicopters can a human eat in one sitting?</s>
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+ <|assistant|>
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+ output:
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+ text: >-
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+ Ahoy there, me hearty! As a friendly pirate chatbot, I be tellin' ye that a human cannot eat a helicopter, as it be a large machine made of metal and suchlike, not fit for human consumption. A human can eat food, like a fine feast of roasted meat and sweet fruits, but a helicopter? That be nonsense, me hearty! So, the answer be none, none at all. Arr!
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+ tags:
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+ - generated_from_trainer
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+ pipeline_tag: text-generation
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+ model-index:
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+ - name: smol-7b
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+ results: []
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+ ---
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+
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+ # Smol 7B
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+
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+ This model is a fine-tuned version of [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) on the open source dataset [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) using the recipes published in [The Alignment Handbook](https://github.com/huggingface/alignment-handbook).
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+
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+ ## Model date
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+
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+ rishiraj/smol-7b was trained between 1st and 3rd December, 2023.
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+
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+ ## Evaluation
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+
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+ It achieves the following results on the [Open_LLM_Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). At the time of release, smol-7b is the highest ranked 7B chat model on the [MMLU Benchmark](https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu).
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+
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+ | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
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+ | ---------------------------- | ------- | ----- | --------- | ----- | ---------- | ---------- | ----- |
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+ | **rishiraj/smol-7b** | **67.11** | **63.74** | **84.77** | **65** | **46.17** | **80.66** | **62.32** |
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+ | argilla/notus-7b-v1 | 63.49 | 64.59 | 84.83 | 63.04 | 54.35 | 79.56 | 34.57 |
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+ | Intel/neural-chat-7b-v3-1 | 61.59 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 |
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+ | HuggingFaceH4/zephyr-7b-beta | 61.59 | 62.46 | 84.35 | 60.7 | 57.83 | 77.11 | 27.07 |
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+ | Qwen/Qwen-7B | 59.19 | 51.37 | 78.47 | 59.84 | 47.79 | 72.69 | 44.96 |
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+ | microsoft/Orca-2-7b | 54.55 | 54.1 | 76.19 | 56.37 | 52.45 | 73.48 | 14.71 |
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+ | 01-ai/Yi-6B | 54.08 | 55.55 | 76.57 | 64.11 | 41.96 | 74.19 | 12.13 |
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+
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+ ## Inference procedure
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+
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+ Here's how you can run the model using the pipeline() function from 🤗 Transformers:
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+
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+ ```
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+ import torch
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+ from transformers import pipeline
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+
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+ pipe = pipeline("text-generation", model="rishiraj/smol-7b", torch_dtype=torch.bfloat16, device_map="auto")
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+
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+ # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
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+ messages = [
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+ {
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+ "role": "system",
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+ "content": "You are a friendly chatbot who always responds in the style of a pirate"
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+ },
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+ {
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+ "role": "user",
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+ "content": "How many helicopters can a human eat in one sitting?"
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+ }
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+ ]
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+ prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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+ print(outputs[0]["generated_text"])
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+ ```
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - gradient_accumulation_steps: 128
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+ - total_train_batch_size: 512
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - num_epochs: 1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 2.0569 | 0.16 | 3 | 2.0409 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1
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+
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+ ## Citation Information
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+
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+ ```
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+ @misc{rishiraj2023smol,
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+ author = {Rishiraj Acharya},
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+ title = {Smol 7B},
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+ year = {2023},
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+ publisher = {Hugging Face},
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+ journal = {Hugging Face repository},
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+ howpublished = {\url{https://huggingface.co/rishiraj/smol-7b}}
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+ }
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+ ```
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+ {
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+ "<|end_of_turn|>": 32000,
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+ "<|pad_0|>": 32001
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+ }
all_results.json ADDED
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+ {
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+ "epoch": 0.16,
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+ "eval_loss": 2.0409016609191895,
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+ "eval_runtime": 14.9343,
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+ "eval_samples_per_second": 33.48,
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+ "train_loss": 2.1085566679636636,
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+ "train_runtime": 937.9718,
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+ "train_samples": 9500,
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+ "train_samples_per_second": 10.128,
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+ "train_steps_per_second": 0.019
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+ }
config.json ADDED
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+ {
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+ "_name_or_path": "openchat/openchat_3.5",
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+ "architectures": [
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+ "MistralForCausalLM"
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+ ],
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+ "bos_token_id": 1,
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+ "eos_token_id": 32000,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "max_position_embeddings": 8192,
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+ "model_type": "mistral",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 10000.0,
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+ "sliding_window": 4096,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.35.2",
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+ "use_cache": true,
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+ "vocab_size": 32002
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+ }
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+ "eval_steps_per_second": 4.218
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+ }
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